Layer-based Composite Reputation Bootstrapping

TL;DR

This paper introduces a layer-based reputation bootstrapping framework for composite services, utilizing machine learning models to predict reputation based on multiple indicators, demonstrating effectiveness with real-world data.

Contribution

It presents a novel framework combining a layer-based indicator analysis with a topology-aware neural network for reputation prediction in composite services.

Findings

Effective reputation prediction with high confidence levels.

Improved accuracy over traditional methods.

Validated with real-world datasets.

Abstract

We propose a novel generic reputation bootstrapping framework for composite services. Multiple reputation-related indicators are considered in a layer-based framework to implicitly reflect the reputation of the component services. The importance of an indicator on the future performance of a component service is learned using a modified Random Forest algorithm. We propose a topology-aware Forest Deep Neural Network (fDNN) to find the correlations between the reputation of a composite service and reputation indicators of component services. The trained fDNN model predicts the reputation of a new composite service with the confidence value. Experimental results with real-world dataset prove the efficiency of the proposed approach.